{"cells":[{"cell_type":"markdown","source":"# Fundamento teorico\n\n**Regresion simple**\n\n$$y= \\beta_0 + \\beta_1 x$$\n\n\n**Regresion multiple**\n$$y= \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 $$\n","metadata":{"id":"nu-Uf-h9ti-K","cell_id":"55ba43bf16ed4efabd33d33e683923a8","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"","metadata":{"id":"K9DgAmDcuENP","cell_id":"c7df8d1b315943dc9ad9336e39bf122e","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"**REGRESIÓN LINEAL SIMPLE**","metadata":{"id":"PIP-gFTq0MM9","cell_id":"1201fac1112e49758c939fd9c3230406","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"#Importacion ded librerias\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline","metadata":{"id":"IfwnkGwq0MNA","cell_id":"b84bce31029d409f8bf7670a181b22d3","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":640,"user_tz":180,"timestamp":1650390482479},"deepnote_cell_type":"code"},"outputs":[],"execution_count":1},{"cell_type":"code","source":"from google.colab import drive\nimport os\ndrive.mount('/content/gdrive')\n# Establecer ruta de acceso en dr\nimport os\nprint(os.getcwd())\nos.chdir(\"/content/gdrive/My Drive\")","metadata":{"id":"wtOszOfb0U5n","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"64efbeb08f7f47bab474ae7f6ac303a8","outputId":"198ea95e-adf4-481d-90df-f14fe9a30700","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":66698,"user_tz":180,"timestamp":1650390550830},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Mounted at /content/gdrive\n/content\n"}],"execution_count":2},{"cell_type":"code","source":"#Importacion de los datos\ndataset = pd.read_csv(\"student_scores.csv\", sep = \",\")","metadata":{"id":"YVTJOBC60MNL","cell_id":"ded424bf532d4b9e907456dec557b3c6","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":776,"user_tz":180,"timestamp":1650392530166},"deepnote_cell_type":"code"},"outputs":[],"execution_count":3},{"cell_type":"code","source":"#Vemos el dataset\ndataset","metadata":{"id":"L2VzgzYo0MNM","colab":{"height":833,"base_uri":"https://localhost:8080/"},"cell_id":"615c4e2b06674db7b9b0504f97ee9f58","outputId":"460b94ff-bfb8-43d3-9af3-8009ee7ad493","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":11,"user_tz":180,"timestamp":1650392531485},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":" Hours Scores\n0 2.5 21\n1 5.1 47\n2 3.2 27\n3 8.5 75\n4 3.5 30\n5 1.5 20\n6 9.2 88\n7 5.5 60\n8 8.3 81\n9 2.7 25\n10 7.7 85\n11 5.9 62\n12 4.5 41\n13 3.3 42\n14 1.1 17\n15 8.9 95\n16 2.5 30\n17 1.9 24\n18 6.1 67\n19 7.4 69\n20 2.7 30\n21 4.8 54\n22 3.8 35\n23 6.9 76\n24 7.8 86","text/html":"\n
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\n \n \n \n Hours \n Scores \n \n \n \n \n 0 \n 2.5 \n 21 \n \n \n 1 \n 5.1 \n 47 \n \n \n 2 \n 3.2 \n 27 \n \n \n 3 \n 8.5 \n 75 \n \n \n 4 \n 3.5 \n 30 \n \n \n 5 \n 1.5 \n 20 \n \n \n 6 \n 9.2 \n 88 \n \n \n 7 \n 5.5 \n 60 \n \n \n 8 \n 8.3 \n 81 \n \n \n 9 \n 2.7 \n 25 \n \n \n 10 \n 7.7 \n 85 \n \n \n 11 \n 5.9 \n 62 \n \n \n 12 \n 4.5 \n 41 \n \n \n 13 \n 3.3 \n 42 \n \n \n 14 \n 1.1 \n 17 \n \n \n 15 \n 8.9 \n 95 \n \n \n 16 \n 2.5 \n 30 \n \n \n 17 \n 1.9 \n 24 \n \n \n 18 \n 6.1 \n 67 \n \n \n 19 \n 7.4 \n 69 \n \n \n 20 \n 2.7 \n 30 \n \n \n 21 \n 4.8 \n 54 \n \n \n 22 \n 3.8 \n 35 \n \n \n 23 \n 6.9 \n 76 \n \n \n 24 \n 7.8 \n 86 \n \n \n
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\n "},"metadata":{},"execution_count":4}],"execution_count":4},{"cell_type":"code","source":"#Shape\ndataset.shape","metadata":{"id":"OQ7T0WXV0MNN","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"6aabbd7855d143818b1a0b34a01829b5","outputId":"27cddc48-1014-4e66-e764-23c49d5c9f48","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":397,"user_tz":180,"timestamp":1650392534641},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"(25, 2)"},"metadata":{},"execution_count":5}],"execution_count":5},{"cell_type":"code","source":"#Analisis estadistico basico\ndataset.describe()","metadata":{"id":"beJ2Cn3L0MNO","colab":{"height":300,"base_uri":"https://localhost:8080/"},"cell_id":"6a673f3a26ef4013ba45a2ec2e22b14c","outputId":"ca36b448-f551-4267-f8d4-a38971f59376","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":6,"user_tz":180,"timestamp":1650392535084},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":" Hours Scores\ncount 25.000000 25.000000\nmean 5.012000 51.480000\nstd 2.525094 25.286887\nmin 1.100000 17.000000\n25% 2.700000 30.000000\n50% 4.800000 47.000000\n75% 7.400000 75.000000\nmax 9.200000 95.000000","text/html":"\n \n
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\n \n \n \n Hours \n Scores \n \n \n \n \n count \n 25.000000 \n 25.000000 \n \n \n mean \n 5.012000 \n 51.480000 \n \n \n std \n 2.525094 \n 25.286887 \n \n \n min \n 1.100000 \n 17.000000 \n \n \n 25% \n 2.700000 \n 30.000000 \n \n \n 50% \n 4.800000 \n 47.000000 \n \n \n 75% \n 7.400000 \n 75.000000 \n \n \n max \n 9.200000 \n 95.000000 \n \n \n
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\n "},"metadata":{},"execution_count":6}],"execution_count":6},{"cell_type":"code","source":"#Ploteamos el dataset\ndataset.plot(x='Hours', y='Scores', style=\"o\")\nplt.title('Hours vs Percentage')\nplt.xlabel('Hours Studied')\nplt.ylabel('Percentage Score')\nplt.axvline(x=5,color='r')\nplt.show()","metadata":{"id":"YL067Z3-0MNO","colab":{"height":295,"base_uri":"https://localhost:8080/"},"cell_id":"922533bec4474cf58f2199e21b9d7d3d","outputId":"c003e6d5-fd23-4b5c-c1d6-2b97d1324640","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":558,"user_tz":180,"timestamp":1650392536871},"deepnote_cell_type":"code"},"outputs":[{"output_type":"display_data","data":{"text/plain":"","image/png":"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\n"},"metadata":{"needs_background":"light"}}],"execution_count":7},{"cell_type":"code","source":"#1) Preparacion de datos\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, 1].values","metadata":{"id":"m19ADokU0MNP","cell_id":"a7b6585792b8425ead2be2f18732b22f","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":4,"user_tz":180,"timestamp":1650392538047},"deepnote_cell_type":"code"},"outputs":[],"execution_count":8},{"cell_type":"code","source":"#2) Empezamos a crear nuestro modelo\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)","metadata":{"id":"UB-zY_rY0MNQ","cell_id":"179778a9216f43cc813a686f8b3c8709","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":1115,"user_tz":180,"timestamp":1650392540476},"deepnote_cell_type":"code"},"outputs":[],"execution_count":9},{"cell_type":"code","source":"# 3) Entrenando el modelo\nfrom sklearn.linear_model import LinearRegression\nregressor = LinearRegression()\nregressor.fit(X_train, y_train)","metadata":{"id":"W_u2YkS-0MNR","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"39db88ae959e44d0b76983d31f59c556","outputId":"59f05a34-1c11-40b1-dd81-b5b99f78de82","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":6,"user_tz":180,"timestamp":1650392540477},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"LinearRegression()"},"metadata":{},"execution_count":10}],"execution_count":10},{"cell_type":"code","source":"#Recuperamos la intersección\nprint(regressor.intercept_)","metadata":{"id":"xNGl42QZ0MNS","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"2a5afd5e523f49799f7cf83a37b80f23","outputId":"98f8d1e5-eaf3-4b4d-c946-5159a0b9cf60","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":4,"user_tz":180,"timestamp":1650392541790},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"2.826892353899737\n"}],"execution_count":11},{"cell_type":"code","source":"#La pendiente\nprint(regressor.coef_)","metadata":{"id":"r2nNftSS0MNS","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"37c89453b3da4e9494fac6c5a272327d","outputId":"90b019ab-3beb-44ad-a979-18c914a78c6f","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":4,"user_tz":180,"timestamp":1650392542254},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"[9.68207815]\n"}],"execution_count":12},{"cell_type":"code","source":"X_test","metadata":{"id":"slrXDvCGuTD5","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"f40d937904e74a0f8d04f4b04b76daf5","outputId":"99c9298a-7ce2-4f75-c7a5-40114352cf8c","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":3,"user_tz":180,"timestamp":1650392542657},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([[8.3],\n [2.5],\n [2.5],\n [6.9],\n [5.9]])"},"metadata":{},"execution_count":13}],"execution_count":13},{"cell_type":"code","source":"y_test","metadata":{"id":"P7E9NIuzuW-q","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"e4831b4792404d1f8a5e7666046592bf","outputId":"6c195397-af83-44c8-d582-8c06efcdbc36","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":366,"user_tz":180,"timestamp":1650392544115},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([81, 30, 21, 76, 62])"},"metadata":{},"execution_count":14}],"execution_count":14},{"cell_type":"code","source":"#Hacemos nuestras predicciones\ny_pred = regressor.predict(X_test)\ny_pred","metadata":{"id":"sFQRf0sq0MNT","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"9188c19728d64a3096679e5b81c414d0","outputId":"7d373a60-92e7-4b96-9247-3d08fbc3be37","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":362,"user_tz":180,"timestamp":1650392544806},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([83.18814104, 27.03208774, 27.03208774, 69.63323162, 59.95115347])"},"metadata":{},"execution_count":15}],"execution_count":15},{"cell_type":"markdown","source":"El y_pred es una matriz numpy que contiene todos los valores predichos para los valores de entrada en la X_test","metadata":{"id":"EIDkZmeC0MNU","cell_id":"5d33142e91474f508b6804b7db2ba288","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"#Convertimos en df la salida\ndf = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\ndf['Sesgo']=df.Actual -df.Predicted\ndf['Error_porc']=((df.Actual -df.Predicted)/df.Actual) *100\ndf","metadata":{"id":"smgatMdo0MNV","colab":{"height":206,"base_uri":"https://localhost:8080/"},"cell_id":"6e7dd6742e7a4a6c9552575781c4852f","outputId":"754ba361-9e25-448d-9fe7-7552d4f0c984","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":11,"user_tz":180,"timestamp":1650392545934},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":" Actual Predicted Sesgo Error_porc\n0 81 83.188141 -2.188141 -2.701409\n1 30 27.032088 2.967912 9.893041\n2 21 27.032088 -6.032088 -28.724227\n3 76 69.633232 6.366768 8.377327\n4 62 59.951153 2.048847 3.304591","text/html":"\n \n
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\n \n \n \n Actual \n Predicted \n Sesgo \n Error_porc \n \n \n \n \n 0 \n 81 \n 83.188141 \n -2.188141 \n -2.701409 \n \n \n 1 \n 30 \n 27.032088 \n 2.967912 \n 9.893041 \n \n \n 2 \n 21 \n 27.032088 \n -6.032088 \n -28.724227 \n \n \n 3 \n 76 \n 69.633232 \n 6.366768 \n 8.377327 \n \n \n 4 \n 62 \n 59.951153 \n 2.048847 \n 3.304591 \n \n \n
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\n "},"metadata":{},"execution_count":16}],"execution_count":16},{"cell_type":"markdown","source":"REGRESIÓN LINEAL MÚLTIPLE","metadata":{"id":"uKXLvubm0MNV","cell_id":"ed2651c8d37f43fdad5310ab7bb0b9d7","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"dataset = pd.read_csv(\"petrol_consumption.csv\", sep = \",\")","metadata":{"id":"O4Jvlbtf0MNW","cell_id":"ee091d17f26246a1862091f3f5347944","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":628,"user_tz":180,"timestamp":1650392547720},"deepnote_cell_type":"code"},"outputs":[],"execution_count":17},{"cell_type":"code","source":"#Vemos el head\ndataset.head()","metadata":{"id":"jhuZhjUH0MNW","colab":{"height":206,"base_uri":"https://localhost:8080/"},"cell_id":"4eb8489816274860935aeb5d71bd2c9f","outputId":"6a7b753e-524d-43cc-8cef-4dfc21f08da1","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":9,"user_tz":180,"timestamp":1650392548917},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":" Petrol_tax Average_income Paved_Highways Population_Driver_licence(%) \\\n0 9.0 3571 1976 0.525 \n1 9.0 4092 1250 0.572 \n2 9.0 3865 1586 0.580 \n3 7.5 4870 2351 0.529 \n4 8.0 4399 431 0.544 \n\n Petrol_Consumption \n0 541 \n1 524 \n2 561 \n3 414 \n4 410 ","text/html":"\n \n
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\n \n \n \n Petrol_tax \n Average_income \n Paved_Highways \n Population_Driver_licence(%) \n Petrol_Consumption \n \n \n \n \n 0 \n 9.0 \n 3571 \n 1976 \n 0.525 \n 541 \n \n \n 1 \n 9.0 \n 4092 \n 1250 \n 0.572 \n 524 \n \n \n 2 \n 9.0 \n 3865 \n 1586 \n 0.580 \n 561 \n \n \n 3 \n 7.5 \n 4870 \n 2351 \n 0.529 \n 414 \n \n \n 4 \n 8.0 \n 4399 \n 431 \n 0.544 \n 410 \n \n \n
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\n "},"metadata":{},"execution_count":18}],"execution_count":18},{"cell_type":"code","source":"dataset.shape","metadata":{"id":"UHeQoz2dv9oZ","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"c1b1da7d98f145fa9453ce2ad82d39a3","outputId":"86269dff-de3a-439b-a50a-051a56f5895a","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":4,"user_tz":180,"timestamp":1650392550255},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"(48, 5)"},"metadata":{},"execution_count":19}],"execution_count":19},{"cell_type":"code","source":"#Estadisticas\ndataset.describe()","metadata":{"id":"3uzSWdsR0MNW","colab":{"height":300,"base_uri":"https://localhost:8080/"},"cell_id":"a505398abfac4e89be2a23f6759abd55","outputId":"674a0124-1aab-43b3-e9af-685d5707f90e","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":368,"user_tz":180,"timestamp":1650392551077},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":" Petrol_tax Average_income Paved_Highways \\\ncount 48.000000 48.000000 48.000000 \nmean 7.668333 4241.833333 5565.416667 \nstd 0.950770 573.623768 3491.507166 \nmin 5.000000 3063.000000 431.000000 \n25% 7.000000 3739.000000 3110.250000 \n50% 7.500000 4298.000000 4735.500000 \n75% 8.125000 4578.750000 7156.000000 \nmax 10.000000 5342.000000 17782.000000 \n\n Population_Driver_licence(%) Petrol_Consumption \ncount 48.000000 48.000000 \nmean 0.570333 576.770833 \nstd 0.055470 111.885816 \nmin 0.451000 344.000000 \n25% 0.529750 509.500000 \n50% 0.564500 568.500000 \n75% 0.595250 632.750000 \nmax 0.724000 968.000000 ","text/html":"\n \n
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\n \n \n \n Petrol_tax \n Average_income \n Paved_Highways \n Population_Driver_licence(%) \n Petrol_Consumption \n \n \n \n \n count \n 48.000000 \n 48.000000 \n 48.000000 \n 48.000000 \n 48.000000 \n \n \n mean \n 7.668333 \n 4241.833333 \n 5565.416667 \n 0.570333 \n 576.770833 \n \n \n std \n 0.950770 \n 573.623768 \n 3491.507166 \n 0.055470 \n 111.885816 \n \n \n min \n 5.000000 \n 3063.000000 \n 431.000000 \n 0.451000 \n 344.000000 \n \n \n 25% \n 7.000000 \n 3739.000000 \n 3110.250000 \n 0.529750 \n 509.500000 \n \n \n 50% \n 7.500000 \n 4298.000000 \n 4735.500000 \n 0.564500 \n 568.500000 \n \n \n 75% \n 8.125000 \n 4578.750000 \n 7156.000000 \n 0.595250 \n 632.750000 \n \n \n max \n 10.000000 \n 5342.000000 \n 17782.000000 \n 0.724000 \n 968.000000 \n \n \n
\n
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\n \n \n \n \n \n \n \n \n\n \n
\n
\n "},"metadata":{},"execution_count":20}],"execution_count":20},{"cell_type":"code","source":"# 1 )Preparación de datos\nX = dataset[['Petrol_tax', 'Average_income', 'Paved_Highways','Population_Driver_licence(%)']]\ny = dataset['Petrol_Consumption']","metadata":{"id":"4ADlRF6t0MNX","cell_id":"a843e49a107946d99333e9a2f54ce304","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":279,"user_tz":180,"timestamp":1650392553127},"deepnote_cell_type":"code"},"outputs":[],"execution_count":21},{"cell_type":"code","source":"# 2) Separacion en train y test\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)","metadata":{"id":"k25OJEmM0MNX","cell_id":"3e80bdc8bb97405f8d5baf189b0b7e66","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":3,"user_tz":180,"timestamp":1650392553542},"deepnote_cell_type":"code"},"outputs":[],"execution_count":22},{"cell_type":"code","source":"#Entrenamiento del modelo\nfrom sklearn.linear_model import LinearRegression\nregressor = LinearRegression()\nregressor.fit(X_train, y_train)","metadata":{"id":"mtIo08po0MNX","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"f1866e611e4145d0a969af3a9042e358","outputId":"1f7732f5-0fe5-4d27-83e6-8007717c1482","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":273,"user_tz":180,"timestamp":1650392555650},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"LinearRegression()"},"metadata":{},"execution_count":23}],"execution_count":23},{"cell_type":"markdown","source":"Como se dijo anteriormente, en caso de regresión lineal multivariable, el modelo de regresión tiene que encontrar los coeficientes más óptimos para todos los atributos. Para ver qué coeficientes ha elegido nuestro modelo de regresión, podemos ejecutar el siguiente script:","metadata":{"id":"DVHaO8yu0MNY","cell_id":"41a7e8922c94492d811737e53610560b","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"# 'Petrol_tax', 'Average_income', 'Paved_Highways','Population_Driver_licence(%)'\nregressor.coef_","metadata":{"id":"wQ_jirk1xBY5","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"4ba886c15d284b019efce74e48ecdb64","outputId":"af9827f5-5f93-4c80-9002-b15dbdd72102","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":7,"user_tz":180,"timestamp":1650392556488},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([-3.69937459e+01, -5.65355145e-02, -4.38217137e-03, 1.34686930e+03])"},"metadata":{},"execution_count":24}],"execution_count":24},{"cell_type":"code","source":"regressor.intercept_","metadata":{"id":"wBh7MxHRxU9k","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"76c9e413864b4cbdbfb82375ec2cec27","outputId":"f24b60eb-f018-40c0-d93c-6a08c5e21b22","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":5,"user_tz":180,"timestamp":1650392558061},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"361.45087906653225"},"metadata":{},"execution_count":25}],"execution_count":25},{"cell_type":"code","source":"X.columns","metadata":{"id":"YdtlK7FDyiHd","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"5dbd800f7dea4839bec200fabf0b8be9","outputId":"6e1faad1-449c-482a-da59-acb808f56652","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":6,"user_tz":180,"timestamp":1650392559150},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Index(['Petrol_tax', 'Average_income', 'Paved_Highways',\n 'Population_Driver_licence(%)'],\n dtype='object')"},"metadata":{},"execution_count":26}],"execution_count":26},{"cell_type":"code","source":"coeff_df = pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficient'])\ncoeff_df","metadata":{"id":"OU4eIQiA0MNY","colab":{"height":175,"base_uri":"https://localhost:8080/"},"cell_id":"8da275b8b505484ca68d28b6b069c506","outputId":"931e5b2b-81d9-4c5b-a46f-0f3205187018","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":8,"user_tz":180,"timestamp":1650392559690},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":" Coefficient\nPetrol_tax -36.993746\nAverage_income -0.056536\nPaved_Highways -0.004382\nPopulation_Driver_licence(%) 1346.869298","text/html":"\n \n
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\n \n \n \n Coefficient \n \n \n \n \n Petrol_tax \n -36.993746 \n \n \n Average_income \n -0.056536 \n \n \n Paved_Highways \n -0.004382 \n \n \n Population_Driver_licence(%) \n 1346.869298 \n \n \n
\n
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\n \n \n \n \n \n \n \n \n\n \n
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\n "},"metadata":{},"execution_count":27}],"execution_count":27},{"cell_type":"code","source":"#Realizando las predicciones\ny_pred = regressor.predict(X_test)\ny_pred","metadata":{"id":"ddBKEWyr0MNZ","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"07ad97d5c69445bb9d2d3a881d20a654","outputId":"c587e788-c260-43e7-8c38-bb281033feec","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":297,"user_tz":180,"timestamp":1650392563387},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([606.69266519, 673.77944169, 584.99149034, 563.53691024,\n 519.05867235, 643.46100256, 572.89761422, 687.07703573,\n 547.6093662 , 530.03762971])"},"metadata":{},"execution_count":28}],"execution_count":28},{"cell_type":"code","source":"y_test","metadata":{"id":"zbTwKgGuzBJC","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"5a259d544fe843309d4faa7d30b1811b","outputId":"652d6363-07ea-48cb-90cd-05afc320c4ae","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":337,"user_tz":180,"timestamp":1650392564974},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"27 631\n40 587\n26 577\n43 591\n24 460\n37 704\n12 525\n19 640\n4 410\n25 566\nName: Petrol_Consumption, dtype: int64"},"metadata":{},"execution_count":29}],"execution_count":29},{"cell_type":"markdown","source":"Para comparar los valores de salida reales X_test con los valores predichos, convertimos en df:","metadata":{"id":"AOslg9Xl0MNZ","cell_id":"8ea147c6ccba4276b8271700b27540c4","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\ndf['Sesgo']=df.Actual -df.Predicted\ndf['Error_porc']=((df.Actual -df.Predicted)/df.Actual) *100\ndf","metadata":{"id":"nLFDzsL6y8A_","colab":{"height":362,"base_uri":"https://localhost:8080/"},"cell_id":"d59c89d738784d9aa362ff35a02cafa8","outputId":"b6278d81-1fec-464c-c9bc-0c1433b693d5","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":286,"user_tz":180,"timestamp":1650392567166},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":" Actual Predicted Sesgo Error_porc\n27 631 606.692665 24.307335 3.852193\n40 587 673.779442 -86.779442 -14.783551\n26 577 584.991490 -7.991490 -1.385007\n43 591 563.536910 27.463090 4.646885\n24 460 519.058672 -59.058672 -12.838842\n37 704 643.461003 60.538997 8.599289\n12 525 572.897614 -47.897614 -9.123355\n19 640 687.077036 -47.077036 -7.355787\n4 410 547.609366 -137.609366 -33.563260\n25 566 530.037630 35.962370 6.353776","text/html":"\n \n
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\n \n \n \n Actual \n Predicted \n Sesgo \n Error_porc \n \n \n \n \n 27 \n 631 \n 606.692665 \n 24.307335 \n 3.852193 \n \n \n 40 \n 587 \n 673.779442 \n -86.779442 \n -14.783551 \n \n \n 26 \n 577 \n 584.991490 \n -7.991490 \n -1.385007 \n \n \n 43 \n 591 \n 563.536910 \n 27.463090 \n 4.646885 \n \n \n 24 \n 460 \n 519.058672 \n -59.058672 \n -12.838842 \n \n \n 37 \n 704 \n 643.461003 \n 60.538997 \n 8.599289 \n \n \n 12 \n 525 \n 572.897614 \n -47.897614 \n -9.123355 \n \n \n 19 \n 640 \n 687.077036 \n -47.077036 \n -7.355787 \n \n \n 4 \n 410 \n 547.609366 \n -137.609366 \n -33.563260 \n \n \n 25 \n 566 \n 530.037630 \n 35.962370 \n 6.353776 \n \n \n
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